Details

Autor(en) / Beteiligte

• Gramacy, Robert B.

Titel

Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences

Auflage

1

Ort / Verlag

Milton: CRC Press

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences is a graduate textbook, or professional handbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), design of experiments, and optimization. Experimentation through simulation, “human out-of-the-loop” statistical support (focusing on the science), management of dynamic processes, online and real-time analysis, automation, and practical application are at the forefront. Features Gaussian process (GP) regression for flexible nonparametric and nonlinear modeling Applications to uncertainty quantification, sensitivity analysis, calibration of computer models to field data, sequential design/active learning, and (blackbox/Bayesian) optimization under uncertainty Advanced topics include treed partitioning, local GP approximation, modeling of simulation experiments (e.g., agent-based models) with coupled nonlinear mean, and variance (heteroskedastic) models Treatment appreciates historical response surface methodology (RSM) and canonical examples, but also emphasizes contemporary methods and implementation in R at a modern scale Rmarkdown facilitates a fully reproducible tour, complete with motivation from, application to, and illustration with compelling real-data examples The book’s presentation targets numerically competent practitioners in engineering, physical sciences, and biological sciences. The writing is statistical in form, but the subjects are not concerning statistics. Rather, they’re about prediction and synthesis under uncertainty, visualization and information, design and decision making, computing, and clean code. Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.